WO2022088181A1 - Procédé de communication, appareil de communication et support de stockage - Google Patents

Procédé de communication, appareil de communication et support de stockage Download PDF

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Publication number
WO2022088181A1
WO2022088181A1 PCT/CN2020/125904 CN2020125904W WO2022088181A1 WO 2022088181 A1 WO2022088181 A1 WO 2022088181A1 CN 2020125904 W CN2020125904 W CN 2020125904W WO 2022088181 A1 WO2022088181 A1 WO 2022088181A1
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WIPO (PCT)
Prior art keywords
model
parameter set
communication method
terminal
indication message
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PCT/CN2020/125904
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English (en)
Chinese (zh)
Inventor
牟勤
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北京小米移动软件有限公司
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Application filed by 北京小米移动软件有限公司 filed Critical 北京小米移动软件有限公司
Priority to PCT/CN2020/125904 priority Critical patent/WO2022088181A1/fr
Priority to US18/034,855 priority patent/US20230422134A1/en
Priority to EP20959341.7A priority patent/EP4240045A4/fr
Priority to CN202080003253.8A priority patent/CN112514441B/zh
Publication of WO2022088181A1 publication Critical patent/WO2022088181A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H04W36/324—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data by mobility data, e.g. speed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • H04W36/0094—Definition of hand-off measurement parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
    • H04W52/0254—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity detecting a user operation or a tactile contact or a motion of the device
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
    • H04W52/0258—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00837—Determination of triggering parameters for hand-off
    • H04W36/008375—Determination of triggering parameters for hand-off based on historical data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present disclosure relates to the field of wireless communication technologies, and in particular, to a communication method, a communication device, and a storage medium.
  • a terminal In order to track channel information, a terminal needs to track at different frequency points, different antenna ports, and different time channels. For example, switching a communication cell or performing cell reselection requires multiple measurements at multiple frequencies, multiple times, and multiple ports.
  • the cell selected during the movement is also relatively fixed.
  • the power of the terminal will be consumed.
  • the present disclosure provides a communication method, a communication device and a storage medium.
  • a communication method which is applied to a terminal, and the method includes:
  • the model for mobility management comprising one or more of a first model, a second model and a third model; the mobility management comprising one or a combination of the following :
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario; wherein the first parameter set includes at least one of the following parameters: location information, application usage information, Cell selection information and channel measurement information; wherein the first parameter set is the input of the first model, and the application scenario is the output of the first model.
  • the predicted application scenario includes:
  • Determine the current first parameter set determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to a second historical parameter set and corresponding measurement parameters; wherein, the second parameter set includes at least one of the following parameters: channel measurement information, time information, Location information, cell ID, and scene information; the measurement parameters include a channel measurement period and/or a measurement port; wherein, the second parameter set is the input of the second model, and the measurement parameters are the second model Output.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to a third historical parameter set and a corresponding first indication message; wherein, the third parameter set includes at least one of the following parameters: time, channel measurement value, Location information, cell ID, and scene information; wherein, the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the model for mobility management is determined in a plurality of the first model, the second model and the third model.
  • the method further includes:
  • a second indication message is received, where the second indication message is used to instruct the terminal to configure multiple models.
  • the configuring a plurality of models includes:
  • the method further includes:
  • a third indication message is received, where the third indication message is used to instruct the terminal to reconfigure one or more models; and according to the third indication message, it is determined to reconfigure one or more models.
  • a communication method applied to a network comprising:
  • the model for mobility management comprising one or more of a first model, a second model and a third model; the mobility management comprising one or a combination of the following : Predict application scenarios, configure measurement parameters, and determine the cell for communication/residence.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario; wherein,
  • the first parameter set includes at least one of the following parameters: location information, application usage information, cell selection information, and channel measurement information;
  • the first parameter set is the input of the first model
  • the application scenario is the output of the first model
  • the predicted application scenario includes:
  • Determine the current first parameter set determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to a second historical parameter set and corresponding measurement parameters; wherein, the second parameter set includes at least one of the following parameters: channel measurement information, time information, Location information, cell ID, and scene information; the measurement parameters include a channel measurement period and/or a measurement port; wherein the second parameter set is the input of the second model, and the measurement parameters are the parameters of the second model output.
  • the configuring measurement parameters includes:
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to a third historical parameter set and a corresponding first indication message; wherein, the third parameter set includes at least one of the following parameters: time, channel measurement value, Location information, cell ID, scene information; the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the determining a cell where the terminal communicates/camps includes:
  • the model for mobility management is determined in a plurality of the first model, the second model and the third model.
  • the method further includes:
  • a second indication message is sent, where the second indication message is used to instruct the terminal to configure multiple models.
  • the configuring a plurality of models includes:
  • the method further comprises:
  • the third indication message it is determined to reconfigure one or more models.
  • a communication apparatus which is applied to a terminal, and the apparatus includes:
  • a terminal determination module configured to determine a model for mobility management, where the model for mobility management includes one or more of a first model, a second model and a third model; the mobility management Include one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario; wherein the first parameter set includes at least one of the following parameters: location information, application usage information, Cell selection information and channel measurement information; wherein the first parameter set is the input of the first model, and the application scenario is the output of the first model.
  • the terminal determination module is used to:
  • Determine the current first parameter set determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to a second historical parameter set and corresponding measurement parameters; wherein, the second parameter set includes at least one of the following parameters: channel measurement information, time information, Location information, cell ID, and scene information; the measurement parameters include a channel measurement period and/or a measurement port; wherein, the second parameter set is the input of the second model, and the measurement parameters are the second model Output.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to a third historical parameter set and a corresponding first indication message; wherein, the third parameter set includes at least one of the following parameters: time, channel measurement value, Location information, cell ID, and scene information; wherein, the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the model for mobility management is determined in a plurality of the first model, the second model and the third model.
  • the apparatus further comprises:
  • a second indication message is received, where the second indication message is used to instruct the terminal to configure multiple models.
  • the configuring a plurality of models includes:
  • the apparatus further comprises:
  • a third indication message is received, where the third indication message is used to instruct the terminal to reconfigure one or more models; and according to the third indication message, it is determined to reconfigure one or more models.
  • a communication apparatus which is applied to a network side, and the apparatus includes:
  • a network determination module for determining a model for mobility management, the model for mobility management including one or more of a first model, a second model and a third model; the mobility management Include one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario; wherein,
  • the first parameter set includes at least one of the following parameters: location information, application usage information, cell selection information, and channel measurement information;
  • the first parameter set is the input of the first model
  • the application scenario is the output of the first model
  • the network determination module is used to:
  • Determine the current first parameter set determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to a second historical parameter set and corresponding measurement parameters; wherein, the second parameter set includes at least one of the following parameters: channel measurement information, time information, Location information, cell ID, and scene information; the measurement parameters include a channel measurement period and/or a measurement port; wherein the second parameter set is the input of the second model, and the measurement parameters are the parameters of the second model output.
  • the network determination module is used to:
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to a third historical parameter set and a corresponding first indication message; wherein, the third parameter set includes at least one of the following parameters: time, channel measurement value, Location information, cell ID, scene information; the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the network determination module is used to:
  • the model for mobility management is determined in a plurality of the first model, the second model and the third model.
  • the apparatus further comprises:
  • a second indication message is sent, where the second indication message is used to instruct the terminal to configure multiple models.
  • the configuring a plurality of models includes:
  • the apparatus further comprises:
  • the third indication message it is determined to reconfigure one or more models.
  • a communication device comprising:
  • processors a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: execute the first aspect or the communication method described in any one of the embodiments of the first aspect; or be configured to: execute The second aspect or the communication method described in any one of the implementation manners of the second aspect.
  • a non-transitory computer-readable storage medium which enables the mobile terminal to execute the first aspect or the first aspect when instructions in the storage medium are executed by a processor of a mobile terminal.
  • the communication method described in any one of the embodiments of the aspect; or the second aspect or the communication method described in any one of the embodiments of the second aspect is performed.
  • the technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: determining at least one model, wherein the at least one model includes a first model, a second model and/or a third model, and the terminal can be determined by using the determined at least one model
  • the frequency point, time and port measurement times can be effectively reduced, thereby reducing the power overhead of the terminal.
  • FIG. 1 is an architectural diagram of a communication system between a network device and a user equipment according to an exemplary embodiment.
  • Fig. 2 is a flow chart of a communication method according to an exemplary embodiment.
  • Fig. 3 is a flowchart showing another communication method according to an exemplary embodiment.
  • Fig. 4 is a block diagram of a communication device according to an exemplary embodiment.
  • Fig. 5 is a block diagram of another communication device according to an exemplary embodiment.
  • Fig. 6 is a block diagram of a communication apparatus according to an exemplary embodiment.
  • Fig. 7 is a block diagram of yet another communication apparatus according to an exemplary embodiment.
  • the terminal In a communication system, in order to track channel information, the terminal needs to track the channel at different frequencies, different antenna ports, and different times.
  • the moving trajectories for some terminals with regular moving trajectories, for example, they go from home to work every morning and from work to home in the evening, and the moving trajectories are also the same. Therefore, the cell selected during the moving process is also relatively fixed. Therefore, for these terminals with fixed mobility rules, when doing mobility management, it is not necessary to perform multiple measurements at multiple frequencies, multiple times and multiple ports during cell handover or cell reselection, which will consume the terminal. of power.
  • the machine learning technology of artificial intelligence is good at discovering the inherent characteristics and connections between data, generating corresponding models, and making corresponding predictions and adjustments based on the models.
  • the self-learning, self-optimizing, and self-decision-making features are also favored by more and more industries.
  • the present disclosure uses various types of service data corresponding to handover/cell reselection, user data characteristics, and various types of function saving parameters to drive machine learning technology to form an optimal parameter configuration model for various types of service data and each user, providing more precise Combine individualized power saving solutions.
  • the communication method provided by the present disclosure can also predict various types of service data, user data, network status, etc. corresponding to handover/cell reselection.
  • the channel measurement parameters are configured, and the terminal handover/cell reselection is determined in advance, and the cell for communication with the terminal is determined, so as to achieve the effect of effectively saving power overhead.
  • FIG. 1 is an architectural diagram of a communication system between a network device and a user equipment according to an exemplary embodiment.
  • the communication method provided by the present disclosure can be applied to the communication system architecture diagram shown in FIG. 1 .
  • the base station may configure a model for the terminal and send a message indicating the model used by the terminal.
  • the terminal determines the configuration model according to the instruction sent by the base station, or determines the used prediction model.
  • the communication system between the network device and the terminal shown in FIG. 1 is only a schematic illustration, and the wireless communication system may also include other network devices, such as core network devices, wireless relay devices, and wireless backhaul devices. Transmission equipment, etc., are not shown in Figure 1.
  • the embodiments of the present disclosure do not limit the number of network devices and the number of terminals included in the wireless communication system.
  • the wireless communication system is a network that provides a wireless communication function.
  • Wireless communication systems can use different communication technologies, such as code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division multiple access (TDMA) , frequency division multiple access (frequency division multiple access, FDMA), orthogonal frequency division multiple access (orthogonal frequency-division multiple access, OFDMA), single carrier frequency division multiple access (single Carrier FDMA, SC-FDMA), carrier sense Carrier Sense Multiple Access with Collision Avoidance.
  • CDMA code division multiple access
  • WCDMA wideband code division multiple access
  • TDMA time division multiple access
  • FDMA frequency division multiple access
  • OFDMA orthogonal frequency division multiple access
  • single carrier frequency division multiple access single Carrier FDMA, SC-FDMA
  • carrier sense Carrier Sense Multiple Access with Collision Avoidance CDMA
  • CDMA code division multiple access
  • WCDMA wideband code division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal
  • the network can be divided into 2G (English: generation) network, 3G network, 4G network or future evolution network, such as 5G network, 5G network can also be called a new wireless network ( New Radio, NR).
  • 2G International: generation
  • 3G network 4G network or future evolution network, such as 5G network
  • 5G network can also be called a new wireless network ( New Radio, NR).
  • New Radio New Radio
  • the present disclosure will sometimes refer to a wireless communication network simply as a network.
  • the wireless access network equipment may be: a base station, an evolved node B (base station), a home base station, an access point (AP) in a wireless fidelity (WIFI) system, a wireless relay A node, a wireless backhaul node, a transmission point (TP) or a transmission and reception point (TRP), etc., can also be a gNB in an NR system, or can also be a component or part of a device that constitutes a base station Wait.
  • the network device may also be an in-vehicle device. It should be understood that, in the embodiments of the present disclosure, the specific technology and specific device form adopted by the network device are not limited.
  • the terminal involved in the present disclosure may also be referred to as terminal equipment, user equipment (User Equipment, UE), mobile station (Mobile Station, MS), mobile terminal (Mobile Terminal, MT), etc.
  • a device that provides voice and/or data connectivity for example, a terminal may be a handheld device with wireless connectivity, a vehicle-mounted device, or the like.
  • some examples of terminals are: Smartphone (Mobile Phone), Pocket Personal Computer (PPC), PDA, Personal Digital Assistant (PDA), notebook computer, tablet computer, wearable device, or Vehicle equipment, etc.
  • the terminal device may also be an in-vehicle device. It should be understood that the embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the terminal.
  • a communication method is provided.
  • Fig. 2 is a flowchart of a communication method according to an exemplary embodiment. As shown in Fig. 2 , the communication method is used in a terminal and includes the following steps.
  • step S11 a model for mobility management is determined.
  • the model includes at least one of the following models: a first model, a second model, and a third model; wherein, the model includes at least one model for mobility management.
  • mobility management includes one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model, the second model, and the third model are pre-built based on historical parameters.
  • the present disclosure refers to the pre-built models as the first model, the second model, and the third model for the convenience of distinction.
  • the first model may include one or more models;
  • the second model may include one or more models;
  • the third model may include one or more models.
  • the relevant parameters of the terminal are predicted by the first model, the second model and/or the third model, and the relevant parameters include at least one of the following:
  • Predict application scenarios configure measurement parameters (eg, channel measurement period, measurement port), and determine the cell where the terminal communicates/camps.
  • determining a cell where the terminal communicates/camps on may include any of the following:
  • the cell for communication/residence is switched, the current cell for communication/residence is maintained, and cell reselection is performed.
  • the first model, the second model, and the third model may be determined by any side of a point, an edge, or an end.
  • the point refers to the terminal
  • the edge refers to the edge computing device
  • the end refers to the central office device; that is to say, any of the above models can be calculated by the terminal device or by the edge computing device. , or it can be calculated by the central office equipment.
  • the any one of the models can be synchronized to any other device in the system; details are not repeated here.
  • an edge refers to an edge computing device, or any device that is located between the central office device and the terminal device after the computing power is sliced.
  • the architecture or connection relationship of the "edge” device in the network is not limited.
  • the "side" device may be a base station device or a data center connecting the base station.
  • the central office device may be a core network device, or may be a data center connected to the core network device.
  • a model for predicting a terminal application scenario is referred to as a first model.
  • the first model is obtained by training according to the historical first parameter set and the corresponding application scenario.
  • the historical first parameter set and the corresponding application scenario are obtained by analyzing the terminal data through a clustering algorithm.
  • the first parameter set includes at least one of the following parameters:
  • Location information application usage information, cell selection information, channel measurement information.
  • the first parameter set is the input of the first model
  • the application scenario is the output of the first model.
  • the application scenario may be a commuting scenario, a stationary scenario, etc.; wherein the commuting scenario may also include a first commuting scenario, a second commuting scenario, and other application scenarios; for example, the first commuting scenario may be a commuting to work scenario;
  • the second commuting scene may be an off-duty commuting scene.
  • the terminal determines the current first parameter set, including one or more of location information, application usage information, cell selection information, and channel measurement information, inputs the first parameter set into the first model, and determines the current application scenario, and send the determined current application scenario to the network side.
  • This embodiment may be implemented in applications as well as in conjunction with other embodiments.
  • the second model is used to configure measurement parameters, where the configured measurement parameters include a channel measurement period and/or a measurement port of the channel.
  • the measurement parameter guides the terminal to perform channel measurement, and then determines a plurality of channel measurement values according to the channel measurement period and/or the measurement port configured by the second model for channel measurement.
  • the second model is obtained by training based on the historical second parameter set and corresponding measurement parameters.
  • the second parameter set used for training the history of the second model and the corresponding measurement parameters are obtained by analyzing the data of the terminal through a clustering algorithm.
  • the second parameter set includes at least one of the following parameters:
  • Channel measurement information time information, location information, cell ID.
  • the measurement parameters include a channel measurement period and/or a measurement port.
  • the second parameter set is the input of the second model, and the measurement parameter is the output of the second model.
  • the second parameter set may be in a one-to-one correspondence with application scenarios, that is, each application scenario corresponds to a set of second parameter sets.
  • each application scenario corresponds to a set of second parameter sets.
  • the second parameter set may be input into the second model to determine the measurement period and/or measurement port configured for the stationary scenario.
  • the input of the second model may be determined according to the application scenario determined by the first model, or may be determined according to other information, and this embodiment may be implemented and the application may be implemented together with other embodiments.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the terminal receives a first indication message sent by the network side, where the first indication message is used to instruct the terminal to determine a cell for communication/residence.
  • the network side determines a first indication message by using the determined third model, and the first indication message instructs the terminal to determine a cell for communication/residence, and perform operations of hold/handover/cell reselection.
  • hold can be understood as the network side instructing the terminal to continue to use the current cell for communication/residence
  • handover can be understood as the network side instructing the terminal to switch to another designated cell that is different from the current communication cell, and the designated cell is used for
  • the terminal communicates
  • the cell reselection can be understood as the network side instructing the terminal to perform a cell reselection operation to re-determine the cell where the terminal communicates or camps on.
  • the terminal determines the hold/handover/cell reselection according to the indication message sent by the network side, so as to determine the cell for communication/residence.
  • the third model is obtained by training according to the third historical parameter set and the corresponding first indication message.
  • the third historical parameter set and the corresponding first indication message are obtained by analyzing the terminal data through a clustering algorithm. It can be understood that there is a one-to-one mapping relationship between the third parameter set and the corresponding first indication message.
  • the third parameter set includes at least one of the following parameters:
  • Time channel measurements, location information, cell ID.
  • the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the current third parameter set of the terminal is input, including one or more of time, channel measurement value, location information, and cell ID, and then it can be determined that the third parameter set corresponds to the third parameter set. the first indication message.
  • the third model determines that the change trend of the channel measurement value included in the input third parameter set is gradually weakening, it is determined that the terminal is at the edge of the current communication cell at this time, and the third model further determines that the handover communication/residence
  • the indication message of the cell, and the indication message is sent to the terminal by the network side to instruct the terminal to switch the cell for communication/residence.
  • the channel measurement value used for the third model input may be determined based on measurement parameters configured by the second model to guide the terminal to perform channel measurement, or may be determined based on measurement parameters configured by predefined information.
  • Measurement parameters include channel measurement period, measurement port, etc.
  • a plurality of channel measurement values are determined according to parameters such as the channel measurement period and the measurement port configured by the second model.
  • three models are used for description, namely: first, the application scenario is determined by the first model, the measurement parameter is determined by the second model, and the first indication message is determined by the third model; those skilled in the art are skilled in the art. It can be understood that these descriptions are only illustrative; those skilled in the art can use an appropriate number of models to determine the first indication parameter, and the embodiments of the present disclosure do not limit the number of models.
  • one model may be trained, or multiple models may be trained. If training a model, the terminal can configure and use the model. If training multiple models, the terminal needs to configure multiple models, and determine the first model to be used among the multiple models. Among them, multiple models are trained based on historical parameter sets and corresponding information.
  • the terminal determining at least one model based on the multiple models may be determined based on an indication message sent by the network side. Wherein, the indication information includes an indicator for indicating at least one model. The terminal determines at least one model corresponding to the indicator according to the indicator for indicating at least one model included in the indication message, and determines the first indication message based on the at least one model.
  • the indicator used to indicate the at least one model may be an explicit indicator, such as bits, symbols, etc., or an implicit indicator, such as implicitly determining the used at least one model through certain signaling.
  • one or more models to be trained can be trained on the terminal, on the network side device, or on the edge computing device; of course, it can also be trained on any of the above three devices Simultaneously on two or three devices.
  • the training model is performed on the network side device
  • the network side sends a second instruction message to configure one or more models.
  • the terminal or any other appropriate device may configure one or more models based on receiving the second indication message sent by the network side.
  • One way of configuring the models is that the second indication message includes the acquisition locations of one or more models, and the terminal configures the one or more models according to the acquisition locations of the models included in the second indication message.
  • Another way to configure models is to configure one or more models based on predefined information.
  • the multiple models to be trained may be based on different time training corresponding models according to terminal usage habits. For example, train a corresponding model for the morning rush hour, train a corresponding model for the noon time, and train a corresponding model for the evening rush hour.
  • a network-side device eg, a base station updates one or more models that have been configured according to the generated new data, and/or retrains at least one model, and updates the updated one or more models. and/or new models to test. If the updated one or more models and/or new models pass the test and can run stably, a third indication message is sent, and the third indication message is used to instruct the terminal to reconfigure one or more models. The terminal determines to reconfigure one or more models according to the received third indication message.
  • a network-side device eg, a base station updates one or more models that have been configured according to the generated new data, and/or retrains at least one model, and updates the updated one or more models. and/or new models to test. If the updated one or more models and/or new models pass the test and can run stably, a third indication message is sent, and the third indication message is used to instruct the terminal to reconfigure one or more models. The terminal determines to reconfigure one or more models according to
  • the embodiments of the present disclosure also provide a communication method.
  • Fig. 3 is a flowchart showing a communication method according to an exemplary embodiment. As shown in Fig. 3 , the communication method is applied to the network side and includes the following steps.
  • step S21 a model for mobility management is determined.
  • the model includes at least one of the following models: a first model, a second model, and a third model; wherein the model includes at least one model for mobility management.
  • mobility management includes one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model, the second model, and the third model are pre-built based on historical parameters.
  • the present disclosure refers to the pre-built models as a first model, a second model, and a third model for the convenience of distinction.
  • the first model may include one or more models;
  • the second model may include one or more models;
  • the third model may include one or more models.
  • the relevant parameters of the terminal are predicted by the first model, the second model and/or the third model, wherein the relevant parameters include at least one of the following:
  • Predict application scenarios configure measurement parameters (eg, channel measurement period, measurement port), and determine the cell where the terminal communicates/camps.
  • determining the cell in which the terminal communicates/camps may include any of the following:
  • the cell for communication/residence is switched, the current cell for communication/residence is maintained, and cell reselection is performed.
  • the first model, the second model, and the third model may be determined by any side of a point, an edge, or an end.
  • the point refers to the terminal
  • the edge refers to the edge computing device
  • the end refers to the central office device; that is to say, any of the above models can be calculated by the terminal device or by the edge computing device. , or it can be calculated by the central office equipment.
  • the any one of the models can be synchronized to any other device in the system; details are not repeated here.
  • an edge refers to an edge computing device, or any device that is located between the central office device and the terminal device after the computing power is sliced.
  • the architecture or connection relationship of the "edge” device in the network is not limited.
  • the "side" device may be a base station device or a data center connecting the base station.
  • the central office device may be a core network device, or may be a data center connected to the core network device.
  • a model for predicting a terminal application scenario is referred to as a first model.
  • the first model is obtained by training according to the historical first parameter set and the corresponding application scenario.
  • the historical first parameter set and the corresponding application scenario are obtained by analyzing the terminal data through a clustering algorithm.
  • the first parameter set includes at least one of the following parameters:
  • Location information application usage information, cell selection information, channel measurement information.
  • the first parameter set is the input of the first model
  • the application scenario is the output of the first model.
  • the application scenario may be a commuting scenario, a stationary scenario, etc.; wherein the commuting scenario may also include a first commuting scenario, a second commuting scenario, and other application scenarios; for example, the first commuting scenario may be a
  • the second commuting scene may be an off-duty commuting scene.
  • the terminal determines the current first parameter set, including one or more of location information, application usage information, cell selection information, and channel measurement information, inputs the first parameter set into the first model, and determines the current application scenario, and send the determined current application scenario to the network side.
  • This embodiment may be implemented in applications as well as in conjunction with other embodiments.
  • the second model is used to configure measurement parameters, where the configured measurement parameters include a channel measurement period and/or a measurement port of the channel.
  • a plurality of channel measurement values are determined according to the channel measurement period and/or the measurement port configured by the second model for measuring the channel.
  • the second model is obtained by training based on the historical second parameter set and corresponding measurement parameters.
  • the second parameter set used for training the history of the second model and the corresponding measurement parameters are obtained by analyzing the data of the terminal through a clustering algorithm.
  • the second parameter set includes at least one of the following parameters:
  • Channel measurement information time information, location information, cell ID.
  • the measurement parameters include a channel measurement period and/or a measurement port.
  • the second parameter set is the input of the second model, and the measurement parameter is the output of the second model.
  • the second parameter set may be in a one-to-one correspondence with application scenarios, that is, each application scenario corresponds to a set of second parameter sets.
  • each application scenario corresponds to a set of second parameter sets.
  • the second parameter set may be input into the second model to determine the measurement period and/or measurement port configured for the stationary scenario.
  • the input of the second model may be determined according to the application scenario determined by the first model, or may be determined according to other information, and this embodiment may be implemented and the application may be implemented together with other embodiments.
  • the current second parameter set is determined according to the application scenario, the second parameter set is input into the second model, and the measurement parameters, including the measurement period and/or the measurement port, are determined. Instruct the terminal to perform channel measurement according to the measurement parameters, and then determine multiple channel measurement values.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the terminal receives the first indication message sent by the network side, where the first indication message is used to instruct the terminal to determine a cell for communication/residence.
  • the network side determines a first indication message by using the determined third model, and the first indication message instructs the terminal to determine a cell for communication/residence, and perform operations of hold/handover/cell reselection.
  • hold can be understood as the network side instructing the terminal to continue to use the current cell for communication/residence
  • handover can be understood as the network side instructing the terminal to switch to another designated cell that is different from the current communication cell, and the designated cell is used for
  • the terminal communicates
  • the cell reselection can be understood as the network side instructing the terminal to perform a cell reselection operation to re-determine the cell where the terminal communicates or camps on.
  • the terminal determines the hold/handover/cell reselection according to the indication message sent by the network side, so as to determine the cell for communication/residence.
  • the third model is obtained by training according to the third historical parameter set and the corresponding first indication message.
  • the third historical parameter set and the corresponding first indication message are obtained by analyzing the terminal data through a clustering algorithm. It can be understood that there is a one-to-one mapping relationship between the third parameter set and the corresponding first indication message.
  • the third parameter set includes at least one of the following parameters:
  • Time channel measurements, location information, cell ID.
  • the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the current third parameter set of the terminal is input, including one or more of time, channel measurement value, location information, and cell ID, and then it can be determined that the third parameter set corresponds to the third parameter set. the first indication message.
  • the third model determines that the change trend of the channel measurement value included in the input third parameter set is gradually weakening, it is determined that the terminal is at the edge of the current communication cell at this time, and the third model further determines that the handover communication/residence
  • the indication message of the cell, and the indication message is sent to the terminal by the network side to instruct the terminal to switch the cell for communication/residence.
  • the channel measurement value used for the third model input may be determined based on measurement parameters configured by the second model to guide the terminal to perform channel measurement, or may be determined based on measurement parameters configured by predefined information.
  • Measurement parameters include channel measurement period, measurement port, etc.
  • a plurality of channel measurement values are determined according to parameters such as the channel measurement period and the measurement port configured by the second model.
  • the network determines multiple channel measurement values determined by the current terminal, inputs the multiple channel measurement values into the third model, and determines the cell where the current terminal communicates/camps according to the third model.
  • three models are used for description, namely: first, the application scenario is determined by the first model, the measurement parameter is determined by the second model, and the first indication message is determined by the third model; those skilled in the art are skilled in the art. It can be understood that these descriptions are only illustrative; those skilled in the art can use an appropriate number of models to determine the first indication parameter, and the embodiments of the present disclosure do not limit the number of models.
  • one model may be trained, or multiple models may be trained. If training a model, the terminal can configure and use the model. If training multiple models, the terminal needs to configure multiple models, and determine the first model to be used among the multiple models. Among them, multiple models are trained based on historical parameter sets and corresponding information.
  • the terminal determining at least one model based on the multiple models may be determined based on an indication message sent by the network side. Wherein, the indication information includes an indicator for indicating at least one model. The terminal determines at least one model corresponding to the indicator according to the indicator for indicating at least one model included in the indication message, and determines the first indication message based on the at least one model.
  • the indicator used to indicate the at least one model may be an explicit indicator, such as bits, symbols, etc., or an implicit indicator, such as implicitly determining the used at least one model through certain signaling.
  • one or more models to be trained can be trained on the terminal, on the network side device, or on the edge computing device; of course, it can also be trained on any of the above three devices Simultaneously on two or three devices.
  • the training model is performed on the network side device
  • the network side sends a second instruction message to configure one or more models.
  • the terminal or any other appropriate device may configure one or more models based on receiving the third indication message sent by the network side.
  • One way of configuring the models is that the second indication message includes the acquisition locations of one or more models, and the terminal configures the one or more models according to the acquisition locations of the models included in the second indication message.
  • Another way to configure models is to configure one or more models based on predefined information.
  • the multiple models to be trained may be based on different time training corresponding models according to terminal usage habits. For example, train a corresponding model for the morning rush hour, train a corresponding model for the midday rush hour, and train a corresponding model for the evening rush hour.
  • a network-side device eg, a base station updates one or more models that have been configured according to the generated new data, and/or retrains at least one model, and updates the updated one or more models. and/or new models to test. If the updated one or more models and/or new models pass the test and can run stably, a third indication message is sent, and the third indication message is used to instruct the terminal to reconfigure one or more models. The terminal determines to reconfigure one or more models according to the received third indication message.
  • a network-side device eg, a base station updates one or more models that have been configured according to the generated new data, and/or retrains at least one model, and updates the updated one or more models. and/or new models to test. If the updated one or more models and/or new models pass the test and can run stably, a third indication message is sent, and the third indication message is used to instruct the terminal to reconfigure one or more models. The terminal determines to reconfigure one or more models according to
  • an embodiment of the present disclosure also provides a communication device.
  • the communication apparatus includes corresponding hardware structures and/or software modules for executing each function.
  • the embodiments of the present disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present disclosure.
  • FIG. 4 is a block diagram of a communication apparatus 100 according to an exemplary embodiment.
  • the apparatus includes a terminal determination module 101 .
  • the terminal determination module 101 is configured to determine a model for performing mobility management, and the model for performing mobility management includes one or more of a first model, a second model and a third model.
  • Mobility management includes one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario.
  • the first parameter set includes at least one of the following parameters: location information, application usage information, cell selection information, and channel measurement information.
  • the first parameter set is the input of the first model, and the application scenario is the output of the first model.
  • the terminal determination module 101 is configured to determine the current first parameter set. Determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to the second historical parameter set and corresponding measurement parameters.
  • the second parameter set includes at least one of the following parameters: channel measurement information, time information, location information, cell ID, and scene information. Measurement parameters include channel measurement period and/or measurement port.
  • the second parameter set is the input of the second model, and the measurement parameter is the output of the second model.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to the third historical parameter set and the corresponding first indication message.
  • the third parameter set includes at least one of the following parameters: time, channel measurement value, location information, cell ID, and scene information.
  • the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • a model for performing mobility management is determined among a plurality of first models, second models, and third models.
  • the communication device further includes:
  • a second indication message is received, where the second indication message is used to instruct the terminal to configure multiple models.
  • a plurality of models are configured for communication, including:
  • the multiple models are configured based on the acquisition locations of the multiple models included in the second indication message. Or, configure multiple models based on predefined information.
  • the communication device further includes:
  • a third indication message is received, where the third indication message is used to instruct the terminal to reconfigure one or more models. According to the third indication message, it is determined to reconfigure one or more models.
  • FIG. 5 is a block diagram of a communication apparatus 200 according to an exemplary embodiment.
  • the apparatus is applied to the network side, and includes a network determination module 201 .
  • the network determining module 201 is configured to determine a model for performing mobility management, where the model for performing mobility management includes one or more of a first model, a second model and a third model.
  • Mobility management includes one or a combination of the following:
  • Predict application scenarios configure measurement parameters, and determine cells for communication/camping.
  • the first model is used to predict application scenarios.
  • the first model is obtained by training according to a historical first parameter set and a corresponding application scenario.
  • the first parameter set includes at least one of the following parameters: location information, application usage information, cell selection information, and channel measurement information.
  • the first parameter set is the input of the first model
  • the application scenario is the output of the first model
  • the network determining module 201 is configured to determine the current first parameter set. Determine the current application scenario according to the first parameter set and the first model.
  • the second model is used to configure measurement parameters, and the measurement parameters are used for the terminal to perform channel measurement.
  • the second model is obtained by training according to the second historical parameter set and corresponding measurement parameters.
  • the second parameter set includes at least one of the following parameters: channel measurement information, time information, location information, cell ID, and scene information. Measurement parameters include channel measurement period and/or measurement port.
  • the second parameter set is the input of the second model, and the measurement parameter is the output of the second model.
  • the network determining module 201 is configured to determine the current second parameter set. Based on the second parameter set and the second model, the measurement parameters are determined.
  • the third model is used to determine the cell in which the terminal communicates/camps.
  • the cell in which the terminal communicates/camps is determined based on the first indication message.
  • the third model is obtained by training according to the third historical parameter set and the corresponding first indication message.
  • the third parameter set includes at least one of the following parameters: time, channel measurement value, location information, cell ID, and scene information.
  • the historical parameter set is the input of the third model, and the corresponding first indication message is the output of the third model.
  • the network determination module is used to:
  • a model for performing mobility management is determined among a plurality of first models, second models, and third models.
  • the communication device further includes:
  • a second indication message is sent, where the second indication message is used to instruct the terminal to configure multiple models.
  • multiple models are configured, including:
  • the multiple models are configured based on the acquisition locations of the multiple models included in the second indication message. Or, configure multiple models based on predefined information.
  • the communication device further includes:
  • a third indication message is received, where the third indication message is used to instruct the terminal to reconfigure one or more models. According to the third indication message, it is determined to reconfigure one or more models.
  • FIG. 6 is a block diagram of an apparatus 300 for channel detection according to an exemplary embodiment.
  • apparatus 300 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, and the like.
  • apparatus 300 may include one or more of the following components: processing component 302, memory 304, power component 306, multimedia component 308, audio component 310, input/output (I/O) interface 312, sensor component 314, and Communication component 316 .
  • the processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 302 may include one or more processors 320 to execute instructions to perform all or some of the steps of the methods described above. Additionally, processing component 302 may include one or more modules that facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302 .
  • Memory 304 is configured to store various types of data to support operations at device 300 . Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, and the like. Memory 304 may be implemented by any type of volatile or non-volatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • Power component 306 provides power to various components of device 300 .
  • Power components 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 300 .
  • Multimedia component 308 includes screens that provide an output interface between the device 300 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
  • the touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action.
  • the multimedia component 308 includes a front-facing camera and/or a rear-facing camera. When the apparatus 300 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.
  • Audio component 310 is configured to output and/or input audio signals.
  • audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when device 300 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 304 or transmitted via communication component 316 .
  • audio component 310 also includes a speaker for outputting audio signals.
  • the I/O interface 312 provides an interface between the processing component 302 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.
  • Sensor assembly 314 includes one or more sensors for providing status assessment of various aspects of device 300 .
  • the sensor assembly 314 can detect the open/closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, and the sensor assembly 314 can also detect a change in the position of the device 300 or a component of the device 300 , the presence or absence of user contact with the device 300 , the orientation or acceleration/deceleration of the device 300 and the temperature change of the device 300 .
  • Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
  • Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor assembly 314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 316 is configured to facilitate wired or wireless communication between apparatus 300 and other devices.
  • Device 300 may access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof.
  • the communication component 316 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication.
  • NFC near field communication
  • the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • apparatus 300 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A gate array
  • controller microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • non-transitory computer-readable storage medium including instructions, such as a memory 304 including instructions, executable by the processor 320 of the apparatus 300 to perform the method described above.
  • the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
  • FIG. 7 is a block diagram of an apparatus 400 for channel detection according to an exemplary embodiment.
  • the apparatus 400 may be provided as a server.
  • apparatus 400 includes a processing component 422, which further includes one or more processors, and a memory resource, represented by memory 432, for storing instructions executable by the processing component 422, such as an application program.
  • An application program stored in memory 432 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 422 is configured to execute instructions to perform the channel detection method described above.
  • Device 400 may also include a power supply assembly 426 configured to perform power management of device 400 , a wired or wireless network interface 450 configured to connect device 400 to a network, and an input output (I/O) interface 458 .
  • Device 400 may operate based on an operating system stored in memory 432, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • first, second, etc. are used to describe various information, but the information should not be limited to these terms. These terms are only used to distinguish the same type of information from one another, and do not imply a particular order or level of importance. In fact, the expressions “first”, “second” etc. are used completely interchangeably.
  • the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information, without departing from the scope of the present disclosure.

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Abstract

La présente divulgation se rapporte à un procédé de communication, à un appareil de communication et à un support de stockage. Le procédé de communication est appliqué à un terminal et consiste : à déterminer un modèle de gestion de mobilité, qui comprend un ou plusieurs modèles parmi des premier à troisième modèles. La gestion de mobilité comprend un élément ou une combinaison des éléments suivants : la prédiction d'un scénario d'application, la configuration d'un paramètre de mesure, et la détermination d'une cellule de communication/résidence. Selon la présente divulgation, le nombre de mesures de points de fréquence, de temps et de ports peut être réduit, et le surdébit de puissance peut être réduit.
PCT/CN2020/125904 2020-11-02 2020-11-02 Procédé de communication, appareil de communication et support de stockage WO2022088181A1 (fr)

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US18/034,855 US20230422134A1 (en) 2020-11-02 2020-11-02 Communication method and communication device
EP20959341.7A EP4240045A4 (fr) 2020-11-02 2020-11-02 Procédé de communication, appareil de communication et support de stockage
CN202080003253.8A CN112514441B (zh) 2020-11-02 2020-11-02 一种通信方法、通信装置及存储介质

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024040617A1 (fr) * 2022-08-26 2024-02-29 Qualcomm Incorporated Généralisation et spécification de modèle d'apprentissage automatique

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113206762B (zh) * 2021-05-06 2023-04-18 广东腾通科技有限公司 一种物联网设备的网络通信模型处理方法及系统
WO2022236638A1 (fr) * 2021-05-10 2022-11-17 北京小米移动软件有限公司 Procédé d'inférence de modèles, appareil d'inférence de modèles, et support d'informations
CN116456355A (zh) * 2022-01-10 2023-07-18 华为技术有限公司 一种通信方法、装置及计算机可读存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105432118A (zh) * 2013-07-31 2016-03-23 高通股份有限公司 使用预测移动性调适移动设备行为
US20190268721A1 (en) * 2018-02-26 2019-08-29 Hewlett Packard Enterprise Development Lp Producing information relating to locations and mobility of devices
CN111651263A (zh) * 2020-02-12 2020-09-11 北京小米移动软件有限公司 移动终端的资源处理方法、装置、计算机设备及存储介质
CN111796701A (zh) * 2019-04-09 2020-10-20 Oppo广东移动通信有限公司 模型训练方法、操作处理方法、装置、存储介质及设备

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9474000B2 (en) * 2013-07-31 2016-10-18 Qualcomm Incorporated Handover and reselection searching using predictive mobility
PT2869647T (pt) * 2013-10-31 2020-11-09 Nokia Technologies Oy Notificação da estimativa de estado de mobilidade ou de informações de histórico de mobilidade
KR102715376B1 (ko) * 2016-12-30 2024-10-11 인텔 코포레이션 라디오 통신을 위한 방법 및 디바이스
US11190287B2 (en) * 2019-02-08 2021-11-30 Qualcomm Incorporated Proactive beam management to avoid channel failure or degraded channel conditions

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105432118A (zh) * 2013-07-31 2016-03-23 高通股份有限公司 使用预测移动性调适移动设备行为
US20190268721A1 (en) * 2018-02-26 2019-08-29 Hewlett Packard Enterprise Development Lp Producing information relating to locations and mobility of devices
CN111796701A (zh) * 2019-04-09 2020-10-20 Oppo广东移动通信有限公司 模型训练方法、操作处理方法、装置、存储介质及设备
CN111651263A (zh) * 2020-02-12 2020-09-11 北京小米移动软件有限公司 移动终端的资源处理方法、装置、计算机设备及存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4240045A4 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024040617A1 (fr) * 2022-08-26 2024-02-29 Qualcomm Incorporated Généralisation et spécification de modèle d'apprentissage automatique

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